ANSYS DesignXplorer Features

ANSYS DesignXplorer technology gives you what you need to explore and optimize your design. We focus on infrastructure, flexibility and ease of use. We have a variety of algorithms for each section because each has particular strengths for specific applications. More detailed coverage of the individual methods can be found in the training materials or in the help manual. This is an overview.

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Design of Experiments

ANSYS DesignXplorer features a variety of DOE types, from basic Latin hypercube sampling (LHS) to central composite design (CCD) factoring to optimal space filling (OSF) â€” even to adaptive sparse grid or kriging methods. These scientific methods subdivide your design space to efficiently develop a series of simulation experiments for exploring designs. The DOE table of design points can be solved in batch mode on your local machine or remotely distributed for a simultaneous solve.

Response Surface

Our powerful response surface methods include full second-order polynomial, kriging, non-parametric regression and neural network approaches. These serve to interpolate between the data points in multidimensional space. They can be visualized as a 2-D or 3-D description of the relationships between design variables and design performance.

ANSYS DesignXplorer can use the response surfaces as a reduced-order model. For example, while looking at optimization trade-offs, the algorithm can search the response surface to rapidly solve many thousands of samples. You can also probe the response surface or add design points at will.

Our adaptive response surface methods, such as Kriging with Auto Refinement or Sparse Grid, will actually refine until sufficient accuracy is achieved. These methods feature a convergence plot.

Optimization

Once you have explored the design and understand correlations and sensitivities, you may want to optimize the design. DesignXplorer includes several algorithms that help identify the most suitable candidates — taking into account multiple objectives and performance trade-offs.

Parallel plots: visualization and interactive manipulation of design variables and product performance on single graph

Multi-objective optimization providing several design candidates

Six Sigma (Probabilistic) Analysis

Simulation often begins with specified deterministic values for dimensions, loads, boundary conditions and material properties. However, in the real world, these values often vary due to manufacturing tolerances or the range of operating conditions. A Six Sigma analysis runs a series of small variations on these inputs and calculates the expected output variation. This can help you to determine whether or not your design meets robustness requirements.